Machine Learning with R and Python

Introduction to Supervised Learning
Introduction to unsupervised learning
Introduction to reinforcement learning
Machine Learning versus Rule-based programming
Understanding What Machine Learning can do using the Tasks Framework
Creating Machine-Learning Models with Python and scikit learn.
Types of datasets used in Machine Learning.
Life Cycle of Machine Learning
Dealing with Missing Values – An example
Standardization and Normalization to Deal with Variables with Different Scales
Types of scaling techniques
Eliminating Duplicate Entries
Learning Rules to Classify Objects?
Understanding Logistic Regression
Applying Logistic Regression to The Iris classification Task
Closing Our First Machine Learning Pipeline with a Simple Model Evaluator
Creating Formulas that predict the Future – A House Price Example
Understanding Linear Regression
Applying Linear Regression to the Boston House Price Task
Evaluating Numerical Predictions with Least Squares
Gradient Descent Algorithm
Batch Gradient Descent
Stochastic Gradient Descent algorithm
Exploring Unsupervised Learning and Its Usefulness
Finding Groups Automatically with k-means clustering
Reducing The Number of variables in your data with PCA
Smooth out your Histograms with kernel Density Estimation
Decision Trees Classifier
Decision Tree Regressor
Random Forest Classifier
Random Forest Regressor
Automatic Feature Engineering with Support Vector Machines
Deal with Nonlinear Relationships with Polynomial Regression
Reduce the number of Learned Rules with Regularization
Using Feature Scaling to Standardize Data
Implementing Feature Engineering with Logistic Regression
Extracting Data with Feature Selection and Interaction
Combining all Together
Build Model Based on Real-world Problems
Support Vector machines
Implementing kNN on the Data set
Decision Tree as Predictive Model
Dimensionality Reduction techniques
Combining all Together
Random Forest for Classification
Gradient Boosting Trees and Bayes Optimization
CatBoost to Handle Categorical Data
Implement Blending
Implement Stacking
Memory-Based Collaborative Filtering
Item-to-Item Recommendation with kNN
Applying Matrix Factorization on Datasets
Word batch for Real-world Problem
Validation Dataset Tuning.
Regularizing model to avoid over fitting
Adversarial Validation
Perform metric Selection on real Data.
Tune a linear model to predict House prices
Tune an SVM to predict a politician’s Party Based on their Voting Record
Splitting your datasets into train, test and validate
Persist Models by Saving Them to Disk
Transform your variable length Features into One-Hot Vectors
Finding the most important Features in your classifier
Predicting Multiple Targets with the Same Dataset
Retrieving the Best Estimators after Grid Search
Extracting Decision Tree Rules from Scikit-learning
Finding out which features are important in Random Forest Model
Classifying with SVMs, when your data has unbalanced classes
Computing True/False Positives/Negatives after in scikit-learn
Labelling Dimensions with Original Feature Names after PCA
Clustering Text Documents with Scikit-learn k-means
Listing Word Frequency in a Corpus Using Only scikit-learn
Polynomial Kernel Regression Using Pipelines
Visualize outputs over two dimensions using Numpy’s Meshgrid
Drawing out a Decision Tree Trained in scikit-learn
Clarify your Histogram by Labeling each Bin
Centralizing Your Color legend when you have multiple subplots
Programming with TENSORFLOW
Implementation of all above models with TENSORFLOW